


import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu







class 	__lowercase    (nn.Module  ):





							"""simple docstring"""







							def __init__(      self       ):





										"""simple docstring"""




										super().__init__()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					nn.Linear(3			,						4       )
										SCREAMING_SNAKE_CASE_		:     int					=					nn.BatchNormad(4       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(4			,						5       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase__       )       )       )







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return (args[0] + 1,) + args[1:], kwargs







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										return output + 1







class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					ModelForTest()
										SCREAMING_SNAKE_CASE_		:     List[str]					=					ModelHook()

										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )
										self.assertEqual(test_model._hf_hook			,						lowerCAmelCase__       )
										self.assertTrue(hasattr(lowerCAmelCase__			,						'_old_forward'       )       )

										# Check adding the hook did not change the name or the signature
										self.assertEqual(test_model.forward.__name__			,						'forward'       )
										self.assertListEqual(list(inspect.signature(test_model.forward       ).parameters       )			,						['x']       )

										remove_hook_from_module(lowerCAmelCase__       )
										self.assertFalse(hasattr(lowerCAmelCase__			,						'_hf_hook'       )       )
										self.assertFalse(hasattr(lowerCAmelCase__			,						'_old_forward'       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ModelForTest()
										SCREAMING_SNAKE_CASE_		:     str					=					ModelHook()

										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )
										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__			,						append=lowerCAmelCase__       )

										self.assertEqual(isinstance(test_model._hf_hook			,						lowerCAmelCase__       )			,						lowerCAmelCase__       )
										self.assertEqual(len(test_model._hf_hook.hooks       )			,						2       )
										self.assertTrue(hasattr(lowerCAmelCase__			,						'_old_forward'       )       )

										# Check adding the hook did not change the name or the signature
										self.assertEqual(test_model.forward.__name__			,						'forward'       )
										self.assertListEqual(list(inspect.signature(test_model.forward       ).parameters       )			,						['x']       )

										remove_hook_from_module(lowerCAmelCase__       )
										self.assertFalse(hasattr(lowerCAmelCase__			,						'_hf_hook'       )       )
										self.assertFalse(hasattr(lowerCAmelCase__			,						'_old_forward'       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ModelForTest()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     Any					=					test_model(x + 1       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					test_model(x + 2       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					PreForwardHook()
										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					test_model(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(lowerCAmelCase__			,						lowerCAmelCase__			,						atol=1E-5       )       )

										# Attaching a hook to a model when it already has one replaces, does not chain
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					PreForwardHook()
										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					test_model(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(lowerCAmelCase__			,						lowerCAmelCase__			,						atol=1E-5       )       )

										# You need to use the sequential hook to chain two or more hooks
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					SequentialHook(PreForwardHook()			,						PreForwardHook()       )
										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					test_model(lowerCAmelCase__       )
										assert torch.allclose(lowerCAmelCase__			,						lowerCAmelCase__			,						atol=1E-5       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					ModelForTest()
										SCREAMING_SNAKE_CASE_		:     int					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     int					=					test_model(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					PostForwardHook()
										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					test_model(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(lowerCAmelCase__			,						output + 1			,						atol=1E-5       )       )

										# Attaching a hook to a model when it already has one replaces, does not chain
										SCREAMING_SNAKE_CASE_		:     Dict					=					PostForwardHook()
										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					test_model(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(lowerCAmelCase__			,						output + 1			,						atol=1E-5       )       )

										# You need to use the sequential hook to chain two or more hooks
										SCREAMING_SNAKE_CASE_		:     Any					=					SequentialHook(PostForwardHook()			,						PostForwardHook()       )
										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					test_model(lowerCAmelCase__       )
										assert torch.allclose(lowerCAmelCase__			,						output + 2			,						atol=1E-5       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					ModelForTest()
										SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					test_model(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					PostForwardHook()
										add_hook_to_module(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					test_model(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(lowerCAmelCase__			,						output + 1       )       )
										self.assertTrue(outputa.requires_grad       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					True
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					test_model(lowerCAmelCase__       )
										self.assertFalse(outputa.requires_grad       )






							@require_multi_gpu
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					ModelForTest()
										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )

										# This will move each submodule on different devices
										add_hook_to_module(model.lineara			,						AlignDevicesHook(execution_device=0       )       )
										add_hook_to_module(model.batchnorm			,						AlignDevicesHook(execution_device=0       )       )
										add_hook_to_module(model.lineara			,						AlignDevicesHook(execution_device=1       )       )

										self.assertEqual(model.lineara.weight.device			,						torch.device(0       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device(0       )       )
										self.assertEqual(model.batchnorm.running_mean.device			,						torch.device(0       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device(1       )       )

										# We can still make a forward pass. The input does not need to be on any particular device
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     str					=					model(lowerCAmelCase__       )
										self.assertEqual(output.device			,						torch.device(1       )       )

										# We can add a general hook to put back output on same device as input.
										add_hook_to_module(lowerCAmelCase__			,						AlignDevicesHook(io_same_device=lowerCAmelCase__       )       )
										SCREAMING_SNAKE_CASE_		:     int					=					torch.randn(2			,						3       ).to(0       )
										SCREAMING_SNAKE_CASE_		:     int					=					model(lowerCAmelCase__       )
										self.assertEqual(output.device			,						torch.device(0       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )

										# This will move each submodule on different devices
										SCREAMING_SNAKE_CASE_		:     int					=					{'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

										add_hook_to_module(model.lineara			,						AlignDevicesHook(**lowerCAmelCase__       )       )
										add_hook_to_module(model.batchnorm			,						AlignDevicesHook(**lowerCAmelCase__       )       )
										add_hook_to_module(model.lineara			,						AlignDevicesHook(**lowerCAmelCase__       )       )

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										# Buffers are not included in the offload by default, so are on the execution device
										SCREAMING_SNAKE_CASE_		:     str					=					torch.device(hook_kwargs['execution_device']       )
										self.assertEqual(model.batchnorm.running_mean.device			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					model(lowerCAmelCase__       )
										self.assertEqual(output.device			,						lowerCAmelCase__       )

										# Removing hooks loads back the weights in the model.
										remove_hook_from_module(model.lineara       )
										remove_hook_from_module(model.batchnorm       )
										remove_hook_from_module(model.lineara       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )

										# Now test with buffers included in the offload
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{
										    'execution_device': 0 if torch.cuda.is_available() else 'cpu',
										    'offload': True,
										    'offload_buffers': True,
										}

										add_hook_to_module(model.lineara			,						AlignDevicesHook(**lowerCAmelCase__       )       )
										add_hook_to_module(model.batchnorm			,						AlignDevicesHook(**lowerCAmelCase__       )       )
										add_hook_to_module(model.lineara			,						AlignDevicesHook(**lowerCAmelCase__       )       )

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.running_mean.device			,						torch.device('meta'       )       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					model(lowerCAmelCase__       )
										self.assertEqual(output.device			,						lowerCAmelCase__       )

										# Removing hooks loads back the weights in the model.
										remove_hook_from_module(model.lineara       )
										remove_hook_from_module(model.batchnorm       )
										remove_hook_from_module(model.lineara       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )

										# This will move each submodule on different devices
										SCREAMING_SNAKE_CASE_		:     str					=					0 if torch.cuda.is_available() else 'cpu'
										attach_align_device_hook(lowerCAmelCase__			,						execution_device=lowerCAmelCase__			,						offload=lowerCAmelCase__       )

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										# Buffers are not included in the offload by default, so are on the execution device
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.device(lowerCAmelCase__       )
										self.assertEqual(model.batchnorm.running_mean.device			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					model(lowerCAmelCase__       )
										self.assertEqual(output.device			,						lowerCAmelCase__       )

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase__       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )

										# Now test with buffers included in the offload
										attach_align_device_hook(lowerCAmelCase__			,						execution_device=lowerCAmelCase__			,						offload=lowerCAmelCase__			,						offload_buffers=lowerCAmelCase__       )

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.running_mean.device			,						torch.device('meta'       )       )

										SCREAMING_SNAKE_CASE_		:     int					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					model(lowerCAmelCase__       )
										self.assertEqual(output.device			,						lowerCAmelCase__       )

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase__       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					ModelForTest()

										# Everything is on CPU
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )

										# This will move each submodule on different devices
										SCREAMING_SNAKE_CASE_		:     Dict					=					0 if torch.cuda.is_available() else 'cpu'
										attach_align_device_hook(
										    lowerCAmelCase__			,						execution_device=lowerCAmelCase__			,						offload=lowerCAmelCase__			,						weights_map=model.state_dict()       )

										# Parameters have been offloaded, so on the meta device
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										# Buffers are not included in the offload by default, so are on the execution device
										SCREAMING_SNAKE_CASE_		:     Dict					=					torch.device(lowerCAmelCase__       )
										self.assertEqual(model.batchnorm.running_mean.device			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					model(lowerCAmelCase__       )
										self.assertEqual(output.device			,						lowerCAmelCase__       )

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase__       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )

										# Now test with buffers included in the offload
										attach_align_device_hook(
										    lowerCAmelCase__			,						execution_device=lowerCAmelCase__			,						offload=lowerCAmelCase__			,						weights_map=model.state_dict()			,						offload_buffers=lowerCAmelCase__			,						)

										# Parameters have been offloaded, so on the meta device, buffers included
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('meta'       )       )
										self.assertEqual(model.batchnorm.running_mean.device			,						torch.device('meta'       )       )

										SCREAMING_SNAKE_CASE_		:     int					=					torch.randn(2			,						3       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					model(lowerCAmelCase__       )
										self.assertEqual(output.device			,						lowerCAmelCase__       )

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase__       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.batchnorm.weight.device			,						torch.device('cpu'       )       )
										self.assertEqual(model.lineara.weight.device			,						torch.device('cpu'       )       )






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
